Many fields encounter problems associated with perceptually tuning a system. For example, in perceptually tuning or “fitting” a hearing assistance device, such as a hearing aid, antiquated methods subjected a single hearing impaired user to many and various audio-related settings of their hearing aid and, often via technical support from an audiologist, individually determined the preferred settings for that single user. This approach, however, has proven itself lacking in universal applicability.
Thus, prescriptive fitting formulas have evolved whereby large numbers of users can become satisfactorily fit by adjusting the same hearing assistance device. With the advent of programmable hearing aids, this approach has become especially more viable. This approach is, however, still too general because individual preferences are often ignored. In one particular hearing assistance device fitting selection strategy, paired comparisons were used. In this strategy, users were presented with a choice between two actual hearing aids from a large set of hearing aids and asked to compare them in an iterative round robin, double elimination tournament or modified simplex procedure until one hearing aid “winner” having optimum frequency-gain characteristics was converged upon. These uses of paired comparisons, however, are extremely impractical in time and financial resources. Moreover, such strategy cannot easily find implementation in an unsupervised home setting by an actual hearing aid user.
In a more recent and very limited selection strategy, genetic algorithms were blended with user input to achieve a hearing assistance device fitting. As is known, and as its name implies, genetic algorithms are a class of algorithms modeled upon living organisms' ability to ensure their evolutionary success via natural selection. In natural selection, the fittest organisms survive while the weakest are killed off. The next generation of organisms (children) are, thus, offspring of the fittest organisms from the previous generation (parents). Genetic algorithm programs for perceptual optimization include a number of possible solutions (or hearing assistance device settings) that comprise a population of genes, and the best potential solutions are passed on to the next generation while the poor solutions died off. In the context of perceptual optimization, the best and worst genes are determined by a user's (human listener's) preferences. Genetic algorithms can use subjective input from a user based on preference levels using paired comparisons.
Previous methods of estimating rank order from paired comparisons depend on the consistency of user judgments. If even one user judgment is incorrect, rank order produced by these previous methods would be severely compromised.
What is needed in the art is a more robust ranking strategy for fitting or tuning hearing assistance devices to individual users' preferred settings. The art needs better genetic algorithm operations for perceptually tuning a system using subjective user judgments.